To facilitate the research, we split the 4,000 IDs of the Gait3D dataset into the train/test subsets with 3,000/1,000 IDs, respectively.
For the test set, we further randomly select one sequence from each ID to build the query set with 1,000 sequences, while the rest of the sequences become the gallery set with 5,369 sequences.
Our evaluation protocol is based on the open-set instance retrieval setting like existing gait recognition datasets [1]} and the person ReID task [2]}.
Given a query sequence, we measure its similarity between all sequences in the gallery set.
Then a ranking list of the gallery set is returned by the descending order of the similarities.
We report the average Rank-1 and Rank-5 identification rates over all query sequences.
We also adopt the mean Average Precision (mAP) and mean Inverse Negative Penalty (mINP) [3]} which consider the recall of multiple instances and hard samples.
